#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # #Licensed under the Apache License, Version 2.0 (the "License"); #you may not use this file except in compliance with the License. #You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #Unless required by applicable law or agreed to in writing, software #distributed under the License is distributed on an "AS IS" BASIS, #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #See the License for the specific language governing permissions and #limitations under the License. import os import random import numpy as np import paddle from ppocr.utils.utility import create_module from copy import deepcopy from .rec.img_tools import process_image import cv2 import sys import signal # handle terminate reader process, do not print stack frame def _reader_quit(signum, frame): print("Reader process exit.") sys.exit() def _term_group(sig_num, frame): print('pid {} terminated, terminate group ' '{}...'.format(os.getpid(), os.getpgrp())) os.killpg(os.getpgid(os.getpid()), signal.SIGKILL) signal.signal(signal.SIGTERM, _reader_quit) signal.signal(signal.SIGINT, _term_group) def reader_main(config=None, mode=None): """Create a reader for trainning Args: settings: arguments Returns: train reader """ assert mode in ["train", "eval", "test"],\ "Nonsupport mode:{}".format(mode) global_params = config['Global'] if mode == "train": params = deepcopy(config['TrainReader']) elif mode == "eval": params = deepcopy(config['EvalReader']) else: params = deepcopy(config['TestReader']) params['mode'] = mode params.update(global_params) reader_function = params['reader_function'] function = create_module(reader_function)(params) if mode == "train": readers = [] num_workers = params['num_workers'] for process_id in range(num_workers): readers.append(function(process_id)) return paddle.reader.multiprocess_reader(readers, False) else: return function(mode) def test_reader(image_shape, img_path): img = cv2.imread(img_path) norm_img = process_image(img, image_shape) return norm_img